A heavy vehicle operation adaptability evaluation method based on highway bearing capacity comprehensive measurement

Abstract

The application discloses a kind of heavy vehicle operation adaptability evaluation methods based on highway bearing capacity comprehensive measurement, comprising the following steps: step 1, highway is laid according to lane survey line, respectively each survey line is divided into several evaluation units evenly;Step 2, using multi-frequency array ground penetrating radar along survey line is evenly detected;Step 3, the qualified rate of evaluation unit is calculated: step 4, the risk occurrence possibility score is calculated, and the corresponding disease risk occurrence possibility evaluation grade is obtained;Step 5, the stability of underground structure is comprehensively evaluated;Step 6, using drop hammer type deflectometer, the structure strength index is calculated;Step 7, according to bearing capacity evaluation grade, the heavy vehicle operation adaptability of the evaluation unit is evaluated.The evaluation method disclosed in the application is free from the dependence of data modeling on highway structure design construction parameters, and can measure the highway with certain service life and degenerative change.

Description

Technical Field

[0001] This invention belongs to the field of comprehensive measurement research on highway load-bearing capacity, and specifically relates to a method for evaluating the operational adaptability of heavy vehicles based on comprehensive measurement of highway load-bearing capacity. Background Technology

[0002] Highways are infrastructure. However, some highways are constructed with insufficient attention to quality, using inferior construction materials and employing less sophisticated construction techniques, failing to strictly meet design standards. Even with strict design and construction requirements, as layered structures completely exposed to the natural environment, long-term exposure to factors such as water, heat, and force can negatively impact their mechanical load-bearing capacity. With the large-scale development of underground space, such as the laying of underground pipelines and the excavation of subway tunnels, highway safety hazards are gradually being exposed. These construction activities disturb the original roadbed soil layers, alter groundwater distribution, and lead to soil loosening, forming cavities and hidden pits. When heavy vehicles are operating, this can cause sudden road surface collapse. Accidents not only disrupt traffic flow but also cause significant damage. Therefore, it is necessary to conduct a comprehensive measurement of the highway's load-bearing capacity before construction begins and, based on this, assess its suitability for heavy vehicle operation.

[0003] The patent "A Method for Monitoring Highway Load-Bearing Capacity" (CN115101147 A) discloses a method for constructing a vehicle-highway dynamic response numerical model to obtain the multi-dimensional dynamic response laws of the highway. It employs artificial neural networks and cluster analysis to obtain a highway load-bearing capacity monitoring and evaluation model. Road surface detection sensors are deployed on test sections, and the collected data is input into the evaluation model to obtain the evaluation results. However, the shortcomings are that establishing the numerical model requires accurately setting the design and construction parameters of each structural layer and pavement of the highway to establish dynamic response laws through theoretical simulation. In actual engineering, these prior parameters are often unavailable or change with the increase in the service life of the highway. Using deep learning algorithms requires establishing a large dataset and accurate manual labeling, and continuous training and verification to achieve good convergence results. This requires a certain amount of prior data accumulation, which is usually difficult to achieve. Road surface detection sensors are generally deployed by pre-embedding or trenching. Pre-embedding can only be used in key monitoring areas, while trenching will damage the integrity of the highway. This makes it unsuitable for actual engineering projects with mobility and randomness. The specific form of the evaluation results, and how they provide guidance or serve as a basis for decision-making, are not mentioned. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for evaluating the operational adaptability of heavy vehicles based on comprehensive measurement of highway load-bearing capacity.

[0005] The present invention adopts the following technical solution:

[0006] An improved method for assessing the operational adaptability of heavy vehicles based on comprehensive measurement of highway load-bearing capacity includes the following steps:

[0007] Step 1: Lay out survey lines along the highway lanes, with each survey line placed at the center line of each lane, and divide each survey line into several evaluation units on an average basis.

[0008] Step 2: Use a multi-frequency array ground-penetrating radar to conduct uniform detection along the survey line. During the detection process, always keep the center of the array antenna aligned with the survey line to obtain the surface layer thickness, base layer disease type, size and depth;

[0009] Step 3: Statistically analyze the surface layer thickness at all sampling locations within each evaluation unit of the highway, and calculate the pass rate of that evaluation unit.

[0010] Let the surface layer thickness measurement value at the i-th sampling position be h. i1 The design value for the surface layer thickness of this road section is h. i0 If h i1 ≥h i0 If the sampling location is qualified, it is considered qualified; otherwise, it is considered unqualified. Therefore, the qualification rate of this evaluation unit is:

[0011]

[0012] The structural layer thickness assessment level of this evaluation unit is obtained based on the table below:

[0013]

[0014] Step 4: Statistically analyze the damage results for each evaluation unit of the highway, calculate the risk probability score, and obtain the corresponding damage risk probability assessment level.

[0015] The probability score P of risk occurrence is calculated using the following formula:

[0016] P = KP A

[0017] In the above formula, K is the coefficient of underground disease type, and P A Indicators for the scale of underground disease structures;

[0018] The corresponding disease risk probability assessment levels are divided according to the following table:

[0019] A 0≤P＜30 It is unlikely to happen in the near future, and the probability of it happening in the long term is very small. B 30≤P＜50 It is unlikely to happen in the near future, but it may happen in the long term. C 50≤P＜70 The likelihood of it occurring in the near future is low, while the likelihood of it occurring in the long term is high. D 70≤P＜90 It is highly likely to happen in the near future. E 90≤P≤100 It is highly likely to happen in the near future.

[0020] Step 5: Based on the structural layer thickness assessment level and the probability assessment level of disease occurrence, conduct a comprehensive evaluation of the underground structure's stability, and obtain the corresponding structural stability assessment level according to the table below:

[0021]

[0022] Step 6: Using a falling weight deflectometer, collect deflection data at the center of the evaluation units with structural stability assessment levels I, II, and III, and calculate the structural strength index PSSI using the following formula:

[0023]

[0024]

[0025] In the above formula, SSR is the pavement structure strength coefficient, l0 is the standard value of pavement deflection, l is the measured value of pavement deflection, α0 is the model parameter, which is 15.71, and α1 is the model parameter, which is -5.19.

[0026] The bearing capacity assessment level of this evaluation unit is obtained according to the table below:

[0027] PSSI ≥90 ≥80，＜90 ≥70，＜80 ≥60，＜70 ＜60

[0028] Step 7: Based on the road surface type and vehicle operating load, assess the heavy vehicle operational adaptability of the evaluation unit according to the load-bearing capacity assessment level:

[0029] Asphalt concrete road

[0030]

[0031] cement concrete road

[0032]

[0033] Grades A and B indicate that the load-bearing capacity of the highway evaluation unit meets the requirements for stability and safety of heavy vehicle operation; Grade C indicates that the load-bearing capacity of the highway evaluation unit basically meets the requirements for stability and safety of heavy vehicle operation; and Grade D indicates that the load-bearing capacity of the highway evaluation unit does not meet the requirements for stability and safety of heavy vehicle operation.

[0034] Furthermore, the evaluation unit in step 1 has a length of 5m.

[0035] Furthermore, the sampling interval in step 2 is 2cm, and the scanning range of the array antenna is 3m.

[0036] Furthermore, in step 4, the coefficient K for the underground disease type is set as follows: 1.0 for voids, 1.0 for cavities, 0.9 for severely loose bodies, 0.7 for water-rich bodies, and 0.5 for generally loose bodies.

[0037] Furthermore, in step 4, the underground disease scale index P A Calculate using the following formula:

[0038] P A =0.7P A1 +0.3P A2

[0039] In the above formula, P A1 P is an indicator of the area of ​​underground diseased tissue. A2 As an indicator of the depth of underground diseased tissue;

[0040] P A1 and P A2 The range of values ​​for is shown in the table below:

[0041]

[0042] The beneficial effects of this invention are:

[0043] Current research on highway load-bearing capacity remains at the theoretical simulation stage, only capable of modeling relatively simple and ideal environments, and cannot exhaustively cover all possibilities. Random highway environments are complex, and there may not be perfectly matching simulation models and results available for comparison and guidance. However, the evaluation method disclosed in this invention eliminates the dependence of data modeling on highway structural design and construction parameters, allowing for measurements of highways with a certain service life and deteriorating characteristics. Based on the measurement information and results, evaluation criteria are established to assess the adaptability of heavy vehicle operations, meeting the requirements of operational mobility, flexibility, and randomness.

[0044] This invention proposes a complete set of measurement procedures and evaluation methods from a practical field perspective. Multi-frequency array ground-penetrating radar and falling-weight deflectometers are used to detect and evaluate the structural and mechanical response characteristics of highways. By dividing the data into evaluation units, the quantitative results, such as detection data and calculated indicators, are correlated with the actual location, enabling rapid screening of usable highway areas. The adaptability to heavy vehicle operations is defined using four levels: A, B, C, and D, which is simple, clear, and directly applicable to decision-making. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the evaluation method disclosed in this invention;

[0046] Figure 2 This is a schematic diagram of the division of evaluation units in step 1 of the evaluation method disclosed in this invention;

[0047] Figure 3 This is a high-frequency ground-penetrating radar structural layer detection data and result diagram;

[0048] Figure 4 This is a diagram showing the detection data and results of defects detected by medium-frequency ground-penetrating radar. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] This invention discloses a method for assessing the operational adaptability of heavy vehicles based on comprehensive measurement of highway load-bearing capacity. It employs a multi-frequency array ground-penetrating radar and a falling-weight deflectometer for comprehensive detection, acquiring data on the depth of the highway subgrade layers and structural strength. Processing yields quantitative information such as structural layer thickness, type, size, and depth of underground defects, and pavement deflection. Based on relevant highway industry standards, evaluation criteria for structural layer thickness, underground defect risk, and pavement structural strength are established. Evaluation units are divided according to length, and the highway load-bearing capacity is evaluated in conjunction with highway type and vehicle operating load, thereby obtaining the operational adaptability assessment level for heavy vehicles. Figure 1 As shown, the specific steps include the following:

[0051] Step 1: Lay out survey lines along the highway according to lanes, with each survey line placed at the center line of each lane, such as... Figure 2 As shown, each measurement line is divided into several evaluation units on average according to the operating length requirements of heavy vehicles;

[0052] Step 2 involves using a multi-frequency array ground-penetrating radar to uniformly probe along the survey line, typically with a sampling interval of 2 cm. Throughout the probe, the center of the array antenna is kept aligned with the survey line. The scanning range of the array antenna is generally 3 meters, covering the entire lane. The high-frequency array antenna is used to detect shallower surface layer information, while the mid-to-low-frequency array antenna is used to detect deeper subgrade defects. The data is then processed using ground-penetrating radar post-processing software to obtain results such as surface layer thickness, subgrade defect type, size, and depth.

[0053] Step 3: Statistically analyze the surface layer thickness at all sampling locations within each evaluation unit of the highway, and calculate the pass rate of that evaluation unit.

[0054] Let the surface layer thickness measurement value at the i-th sampling position be h. i1 The design value for the surface layer thickness of this road section is h. i0 If h i1 ≥h i0 If the sampling location is qualified, it is considered qualified; otherwise, it is considered unqualified. Therefore, the qualification rate of this evaluation unit is:

[0055]

[0056] Based on the structural layer thickness evaluation criteria in the table below, the structural layer thickness assessment level of this evaluation unit is obtained:

[0057]

[0058] Step 4: Statistically analyze the damage results for each evaluation unit of the highway, calculate the risk probability score according to the corresponding evaluation criteria, and obtain the corresponding damage risk probability assessment level.

[0059] The probability score P of risk occurrence is calculated using the following formula:

[0060] P = KP A

[0061] In the above formula, K is the coefficient for the type of underground disease body, with 1.0 for voids, 1.0 for cavities, 0.9 for severely loose bodies, 0.7 for water-rich bodies, and 0.5 for generally loose bodies; P A The scale index of underground diseased tissue is calculated using the following formula:

[0062] P A =0.7P A1 +0.3P A2

[0063] In the above formula, P A1 P is an indicator of the area of ​​underground diseased tissue. A2 As an indicator of the depth of underground diseased tissue;

[0064] P A1 and P A2 The range of values ​​for is shown in the table below:

[0065]

[0066] The corresponding disease risk probability assessment levels are divided according to the following table:

[0067] A 0≤P＜30 It is unlikely to happen in the near future, and the probability of it happening in the long term is very small. B 30≤P＜50 It is unlikely to happen in the near future, but it may happen in the long term. C 50≤P＜70 The likelihood of it occurring in the near future is low, while the likelihood of it occurring in the long term is high. D 70≤P＜90 It is highly likely to happen in the near future. E 90≤P≤100 It is highly likely to happen in the near future.

[0068] Step 5: Based on the structural layer thickness assessment level and the probability assessment level of disease occurrence, conduct a comprehensive evaluation of the underground structure's stability, and obtain the corresponding structural stability assessment level according to the table below:

[0069]

[0070] Grades I and II indicate no obvious defects and a reliable structure. Grade III indicates few defects and a relatively stable structure, but no risk. Grades IV and V indicate obvious defects, a less stable structure, and potential risks. Areas with structural stability assessment grades IV and V restrict the use of heavy vehicles and should be excluded from further assessment.

[0071] Step 6: Using a falling weight deflectometer, collect deflection data near the center of the evaluation unit with structural stability assessment levels I, II, and III, and calculate the structural strength index PSSI using the following formula:

[0072]

[0073]

[0074] In the above formula, SSR is the pavement structure strength coefficient, l0 is the standard value of pavement deflection (0.01mm), l is the measured value of pavement deflection (0.01mm), α0 is the model parameter, which is 15.71, and α1 is the model parameter, which is -5.19.

[0075] The bearing capacity assessment level of this evaluation unit is obtained according to the table below:

[0076] PSSI ≥90 ≥80，＜90 ≥70，＜80 ≥60，＜70 ＜60

[0077] Step 7: Based on the road surface type and vehicle operating load, assess the heavy vehicle operational adaptability of the evaluation unit according to the load-bearing capacity assessment level:

[0078] Asphalt concrete road

[0079]

[0080] cement concrete road

[0081]

[0082] Grades A and B indicate that the load-bearing capacity of the highway evaluation unit meets the stability and safety requirements for heavy vehicle operations. Under the impact of equivalent loads, the mechanical deformation of the highway is minimal and does not affect the execution of operational tasks. Grade C indicates that the load-bearing capacity of the highway evaluation unit basically meets the stability and safety requirements for heavy vehicle operations. Under the impact of equivalent loads, although the highway may deform, it is generally within the allowable range and will not affect the execution of operational tasks. Grade D indicates that the load-bearing capacity of the highway evaluation unit does not meet the stability and safety requirements for heavy vehicle operations. Under the impact of equivalent loads, the highway may experience severe deformation or even breakage, and the smooth execution of operational tasks cannot be guaranteed.

[0083] Example 1, with Figure 2 Taking the evaluation and grading of data in the first evaluation unit as an example:

[0084] Step 1: Lay out survey lines for highways of grade 1 with asphalt concrete surface layer. Select an evaluation unit length of 5m according to the minimum length required for heavy vehicle operation.

[0085] Step 2: Use a multi-frequency array ground-penetrating radar for uniform detection. The antenna array covers a width of 3m and the sampling interval is 2cm. Figure 3 This consists of high-frequency data and processing results collected by ground-penetrating radar in the first four evaluation units. Ground-penetrating radar post-processing software was used to detect and track structural layers, calculate the surface layer thickness at each sampling location, and plot layer thickness curves. Figure 4 This consists of intermediate frequency data and processing results collected by ground-penetrating radar in the first four evaluation units. Using ground-penetrating radar post-processing software, a cavity defect is delineated, and its size and location can be viewed.

[0086] Step 3: Analyze the surface layer thickness and calculate the pass rate. The pass rate for the first evaluation unit is 82%. Figure 3 Therefore, the structural layer thickness assessment level of the first evaluation unit is level 2.

[0087] Step 4: Statistically analyze the disease results and calculate the probability score of risk occurrence. For example... Figure 4 The first evaluation unit has no diseases, and the probability of disease occurrence score P = 0. Therefore, the disease risk occurrence probability assessment level of the first evaluation unit is Grade A.

[0088] Step 5: Based on the structural layer thickness assessment level and the probability assessment level of disease occurrence, a comprehensive evaluation of the underground structure stability is conducted, and the structural stability assessment level of the first evaluation unit is obtained as Level I.

[0089] Step 6: Using a falling weight deflectometer, deflection data was collected near the center of the first evaluation unit, yielding a single-point deflection (0.01 mm) of 12.5. The calculated structural strength index (PSSI) was 99.6. Therefore, the load-bearing capacity assessment level of the first evaluation unit is excellent.

[0090] Step 7: When the vehicle's operating load is 400 tons, the heavy vehicle operation adaptability assessment level of the first evaluation unit is B, which meets the requirements for the stability and safety of heavy vehicle operation.

[0091] Example 2, with Figure 2 Taking the evaluation and grading of data in the third evaluation unit as an example:

[0092] Step 1: For highways of grade 1 with asphalt concrete surface layer, select an evaluation unit length of 5m based on the minimum length required for heavy vehicle operations.

[0093] Step 2: Use a multi-frequency array ground-penetrating radar for uniform detection. The antenna array covers a width of 3m, and the sampling interval is set to 2cm. Figure 3 This consists of high-frequency data and processing results collected by ground-penetrating radar in the first four evaluation units. Ground-penetrating radar post-processing software was used to detect and track structural layers, calculate the surface layer thickness at each sampling location, and plot layer thickness curves. Figure 4 This consists of intermediate frequency data and processing results collected by ground-penetrating radar in the first four evaluation units. Using ground-penetrating radar post-processing software, a cavity defect is delineated, and its size and location can be viewed.

[0094] Step 3: Analyze the surface layer thickness and calculate the pass rate. The pass rate for the third evaluation unit was 94.4%. Figure 3 Therefore, the structural layer thickness assessment level of the third evaluation unit is level 1.

[0095] Step 4: Statistically analyze the disease results and calculate the probability score of risk occurrence. For example... Figure 4 In the third evaluation unit, there is a cavity defect with a depth of 1.12m and an area of ​​1.89 * 1.27 = 2.4m². 2 The underground disease area index P A1 The value is 35, and the depth index P of underground disease bodies is... A2 The value is 85. The underground disease scale index P is calculated. A =50, the probability score of disease occurrence P = 50. Therefore, the disease risk occurrence probability assessment level of the third evaluation unit is level C.

[0096] Step 5: Based on the structural layer thickness assessment level and the probability assessment level of disease occurrence, a comprehensive evaluation of the underground structure stability is conducted, and the structural stability assessment level of the third evaluation unit is obtained as Level II.

[0097] Step 6: Using a falling weight deflectometer, deflection data was collected near the center of the third evaluation unit, yielding a single-point deflection (0.01 mm) of 29. The calculated structural strength index (PSSI) was 69.5, thus the load-bearing capacity assessment level of the third evaluation unit was rated as Grade II.

[0098] Step 7: When the vehicle's operating load is 400 tons, the heavy vehicle operation adaptability assessment level of the third evaluation unit is D, which cannot guarantee the stability and safety of heavy vehicle operation.

Claims

1. A method for evaluating the operational adaptability of heavy vehicles based on comprehensive measurement of highway carrying capacity, characterized in that, Includes the following steps: Step 1: Lay out survey lines along the highway lanes, with each survey line placed at the center line of each lane, and divide each survey line into several evaluation units on an average basis. Step 2: Use a multi-frequency array ground-penetrating radar to conduct uniform detection along the survey line. During the detection process, always keep the center of the array antenna aligned with the survey line to obtain the surface layer thickness, base layer disease type, size and depth; Step 3: Statistically analyze the surface layer thickness at all sampling locations within each evaluation unit of the highway, and calculate the pass rate of that evaluation unit. Let the surface layer thickness measurement value at the i-th sampling position be h. i1 The design value for the surface layer thickness of this road section is h. i0 If h i1 ≥h i0 If the sampling location is qualified, it is considered qualified; otherwise, it is considered unqualified. Therefore, the qualification rate of this evaluation unit is: The structural layer thickness assessment level of this evaluation unit is obtained based on the table below: Step 4: Statistically analyze the damage results for each evaluation unit of the highway, calculate the risk probability score, and obtain the corresponding damage risk probability assessment level. The probability score P of risk occurrence is calculated using the following formula: P=KP A In the above formula, K is the coefficient of underground disease type, and P A Indicators for the scale of underground disease structures; The corresponding disease risk probability assessment levels are divided according to the following table: Step 5: Based on the structural layer thickness assessment level and the probability assessment level of disease occurrence, conduct a comprehensive evaluation of the underground structure's stability, and obtain the corresponding structural stability assessment level according to the table below: Step 6: Using a falling weight deflectometer, collect deflection data at the center of the evaluation units with structural stability assessment levels I, II, and III, and calculate the structural strength index PSSI using the following formula: In the above formula, SSR is the pavement structure strength coefficient, l0 is the standard value of pavement deflection, l is the measured value of pavement deflection, α0 is the model parameter, which is 15.71, and α1 is the model parameter, which is -5.

19. The bearing capacity assessment level of this evaluation unit is obtained according to the table below: Step 7: Based on the road surface type and vehicle operating load, assess the heavy vehicle operational adaptability of the evaluation unit according to the load-bearing capacity assessment level: Asphalt concrete road cement concrete road Grades A and B indicate that the load-bearing capacity of the highway evaluation unit meets the requirements for stability and safety of heavy vehicle operation; Grade C indicates that the load-bearing capacity of the highway evaluation unit basically meets the requirements for stability and safety of heavy vehicle operation; and Grade D indicates that the load-bearing capacity of the highway evaluation unit does not meet the requirements for stability and safety of heavy vehicle operation.

2. The method for evaluating the operational adaptability of heavy vehicles based on comprehensive measurement of highway carrying capacity according to claim 1, characterized in that: The evaluation unit in step 1 is 5m long.

3. The method for assessing the operational adaptability of heavy vehicles based on comprehensive measurement of highway carrying capacity according to claim 1, characterized in that: The sampling interval in step 2 is 2cm, and the scanning range of the array antenna is 3m.

4. The method for assessing the operational adaptability of heavy vehicles based on comprehensive measurement of highway carrying capacity according to claim 1, characterized in that, Step 4: The coefficient K for the underground disease type is set as follows: 1.0 for voids, 1.0 for cavities, 0.9 for severely loose bodies, 0.7 for water-rich bodies, and 0.5 for generally loose bodies.

5. The method for evaluating the operational adaptability of heavy vehicles based on comprehensive measurement of highway carrying capacity according to claim 1, characterized in that, Step 4: Scale index P of underground diseased organisms A Calculate using the following formula: P A =0.7P A1 +0.3P A2 In the above formula, P A1 P is an indicator of the area of ​​underground diseased tissue. A2 As an indicator of the depth of underground diseased tissue; P A1 and P A2 The range of values ​​for is shown in the table below:

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